1. Definition of Interest Rate
The interest rate $r$ can be interpreted as:
- A required rate of return: The investor’s required compensation.
- A discount rate: The rate used to determine the present value of future cash flows.
- An opportunity cost: The return foregone by investing in a particular asset instead of the next best alternative.
These reflect the relationship between cashflows.
2. The Interest Rate and Time Value of Money
$$\text{Interest rate} = \text{Risk-free rate} + \text{Premiums (bearing distinct types of risk)}$$
Also called the yield.
Example: $9500 today equals $10000 in one year → this is the discount rate.
$$\frac{10{,}000 - 9{,}500}{9{,}500} = \frac{500}{9{,}500} \approx 5.26% \quad \text{(rate of return)}$$
2.1 Determinants of Interest Rates
$$r = r_{RF} + \text{Inflation premium} + \text{Default risk premium} + \text{Liquidity premium} + \text{Maturity premium}$$
| Premium | Compensates investors for… |
|---|---|
| Real risk-free rate $r_{RF}$ | Time preference only — assumes no inflation |
| Inflation premium | Expected (average) inflation rate |
| Default risk premium | Possibility that the borrower fails to make a promised payment at the contracted time and amount |
| Liquidity premium | Risk of receiving less than fair value if the investment needs to be converted to cash quickly |
| Maturity premium | Long-term bonds expose investors to more interest rate risk; investors are locked to a fixed rate even when better rates appear in the market |
2.2 Nominal vs. Real Risk-Free Rate
The nominal risk-free interest rate reflects both the real risk-free rate and the inflation premium.
Fisher equation (exact):
$$1 + i = (1 + r_{RF})(1 + \pi)$$
$r_{RF}$: Nominal risk-free rate
$\pi$: Inflation rate
Approximation (when $r_{RF}$ and $\pi$ are small):
$$i \approx r_{RF} + \pi$$
since $(1 + r_{RF})(1 + \pi) = 1 + r_{RF} + \pi + r_{RF}\pi \approx 1 + r_{RF} + \pi$.
as $r_{RF}\pi$ is very small and can be ignored.
3. Rates of Return
Calculate and interpret different approaches to return measurement over time and describe their appropriate uses.
Financial assets generate returns through:
graph TD
A[Financial Asset Return] --> B[Periodic Income]
A --> C[Price Movement]
B --> D[Cash dividends]
B --> E[Interest payments]
B --> F[Pension plans / retirement annuities]
C --> G[Capital gain / loss]
C --> H[Non-dividend-paying stocks]
3.1 Holding Period Return
$$R = \frac{P_1 - P_0 + I_1}{P_0}$$
where $P$ is the price and $I_1$ is the income; we buy at time 0 and sell at time 1.
For periods longer than 1 year, with annual returns $R_1, R_2, R_3$:
$$R = \left[(1+R_1)(1+R_2)(1+R_3)\right] - 1$$
3.2 Arithmetic Mean Return
For holding-period returns (daily, monthly, annual), the arithmetic mean (mean return) is:
$$\bar{R}i = \frac{R{i,1} + R_{i,2} + \cdots + R_{i,T}}{T} = \frac{1}{T}\sum_{t=1}^{T} R_{i,t}$$
Assumes the amount invested at the beginning of each period is the same.
3.3 Geometric Mean Return
$$\bar{R}{G,i} = \sqrt[T]{\prod{t=1}^{T}(1 + R_{i,t})} - 1$$
Example:
$$\sqrt[3]{(1-0.50)(1+0.35)(1+0.27)} - 1 \approx -0.0500 \quad \text{(more precise)}$$
The geometric mean is always less than or equal to the arithmetic mean.
The two are equal only when there is no variability in the observations.
3.4 Harmonic Mean
$$\bar{X}H = \frac{n}{\displaystyle\sum{i=1}^{n}\frac{1}{X_i}}, \quad X_i > 0 \text{ for } i = 1, 2, \ldots, n$$
Example: for ${1, 2, 3, 4, 5, 6, 1000}$, $\bar{X}_H = 2.8560$ (outlier has less impact).
Properties:
- Measure of central tendency in the presence of outliers
- Appropriate for averaging ratios (“amount per unit”)
- Works only for non-negative numbers
Key inequality:
$$\bar{X}_A \geq \bar{X}_G \geq \bar{X}_H$$
$$\bar{X}_A \times \bar{X}_H = \bar{X}_G^2$$
Mean speed example (travel from A to B at speed $v_1$, return at speed $v_2$):
$$\text{mean speed} = \frac{2}{\dfrac{1}{v_1} + \dfrac{1}{v_2}} = \frac{2v_1 v_2}{v_1 + v_2}$$
3.5 Trimmed and Winsorized Means
| Method | Description |
|---|---|
| Trimmed mean | Removes a small defined percentage of the most extreme observations to limit outlier effects |
| Winsorized mean | Replaces extreme observations with the boundary values to limit outlier effects |
Both reduce outlier impact while retaining all data points (Winsorized) or sample size (Trimmed).